How can I maximize token usage in the GPT-3.5 API?

Below is an explanation based on the official API documentation.

Default for max_tokens

• Text Completions Endpoint:
  For the standard text completions endpoint (e.g., GPT‑3.5 text completions), if you do not specify a value, the default for the (deprecated) parameter has historically been 16 tokens. However, note that this parameter is now being replaced by max_completion_tokens in newer chat endpoints.

• Chat Completions:
  In chat-based endpoints, you’re encouraged to use max_completion_tokens. There isn’t a “maximum available” default. If you omit the parameter, the service does not automatically generate up to the full context window; instead, it applies a default value (which may be low).

Using Maximum Tokens Available

If your intention is to allow the AI to generate as many tokens as possible (subject to the model’s overall context length), then you must:

1. Determine how many tokens your prompt already uses.
2. Subtract that number from the model’s maximum context length.
3. Set that result as max_completion_tokens (or max_tokens if you’re using that older parameter).

There is no built‐in “auto‐use all remaining tokens” option in the API.

How the max_tokens Parameter Works

• Hard Limit:
  The max_tokens (or max_completion_tokens) parameter acts as a hard limit. The model generates tokens until it reaches that limit and then stops—there is no “continue generating” if that limit is hit. It does not “try” to avoid going over; it simply won’t produce more than the specified amount.

• Response Behavior:
  The AI does not adjust its output length based on its awareness of max_tokens; it just stops once the count is reached. This behavior ensures that you won’t accidentally exceed the token budget, but it also means that you must calculate or set your limit if you want the full use of the remaining tokens.

Summary

• The default value for max_tokens (in text completions) is typically 16—not the maximum available.
• To have the model use the maximum tokens available to it, you must calculate the remaining tokens (model context length minus prompt tokens) and set that value explicitly.
• The max_tokens/max_completion_tokens parameter is a hard limit; once the token count hits that threshold, the response generation stops.

Feel free to ask if you need further clarification or examples.